LiDAR sensors in computer vision.
The massive diffusion of LiDAR sensors in computer vision has begun with the release of the KITTI dataset [1]}, an extensive collection of several thousand images and pointclouds acquired by a moving car equipped with a Velodyne LiDAR and stereo cameras.
Eventually, more datasets followed this seminal work, such as DrivingStereo [2]}, Argoverse [3]}, Apolloscape [4]} and DSEC [5]}.
In between, over the KITTI dataset many computer vision tasks have been tackled, such as LiDAR SLAM [6]}, [7]}, 3D object detection [8]}, [9]}, semantic segmentation [10]}, [11]}, object tracking [12]}, [13]}, 3D scene flow [14]}, [15]}, fusion of LiDAR measurement with stereo [16]}, [17]}, [18]} or optical flow [19]} and depth completion [20]}, [21]}, [22]}, [23]}, [24]}. The latter is one of the iconic problems in this field and processes LiDAR pointclouds projected into depth maps over the image plane as input. The standard benchmark for this task is hosted by KITTI [20]} and has been obtained in a semi-automatic manner from Velodyne raw data by accumulating multiple point clouds and filtering outliers/moving objects employing consistency with a stereo algorithm [26]}. As a result, ground-truth maps of the completion benchmark miss several labels where stereo algorithm struggle.
